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Related Experiment Video

Updated: Jan 22, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Segmentation of Glomeruli Within Trichrome Images Using Deep Learning.

Shruti Kannan¹, Laura A Morgan², Benjamin Liang²

¹Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.

Kidney International Reports

|July 19, 2019

Summary

We developed a deep learning framework to automate the identification and segmentation of glomeruli in kidney biopsies. This AI approach offers a more accurate and efficient method for renal pathology analysis, improving diagnostic capabilities.

Keywords:

computational pathology deep learning digital pathology glomerulus image segmentation kidney biopsy trichrome stain

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Single-channel Analysis and Calcium Imaging in the Podocytes of the Freshly Isolated Glomeruli

Single-channel Analysis and Calcium Imaging in the Podocytes of the Freshly Isolated Glomeruli

Published on: June 27, 2015

Area of Science:

Nephrology
Digital Pathology
Artificial Intelligence in Medicine

Background:

Glomerular assessment in renal biopsies is crucial for diagnosis and prognosis.
Current manual methods are labor-intensive, subjective, and lack standardization.
Digitized kidney biopsy images offer potential for automated analysis.

Purpose of the Study:

To develop and validate a deep learning framework for automated glomeruli identification and segmentation in human kidney biopsies.
To improve the accuracy and efficiency of renal pathology reporting.

Main Methods:

A convolutional neural network (CNN) was trained on trichrome-stained renal biopsy images from 171 patients.
Images were manually annotated by experts into three categories: no glomerulus, normal/partially sclerosed (NPS), and globally sclerosed (GS).
The CNN model was used to segment globally sclerosed glomeruli in test images.

Main Results:

The CNN model achieved high accuracy in discriminating between glomerular and non-glomerular images (92.67% ± 2.02%).
The segmentation model accurately identified globally sclerosed glomeruli with a Matthews correlation coefficient of 0.628.
The framework demonstrated robust performance on digitized human kidney biopsy data.

Conclusions:

Deep learning provides a powerful tool for analyzing complex histological structures in digitized kidney biopsies.
This automated approach has the potential to standardize and enhance renal pathology assessments.
The developed framework shows promise for clinical application in nephrology.